Frankel Mitchell A, Lehmkuhle Mark J, Spitz Mark C, Newman Blake J, Richards Sindhu V, Arain Amir M
Epitel, Inc., Salt Lake City, UT, United States.
Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, United States.
Front Neurol. 2021 Oct 18;12:728484. doi: 10.3389/fneur.2021.728484. eCollection 2021.
Epitel has developed Epilog, a miniature, wireless, wearable electroencephalography (EEG) sensor. Four Epilog sensors are combined as part of Epitel's Remote EEG Monitoring platform (REMI) to create 10 channels of EEG for remote patient monitoring. REMI is designed to provide comprehensive spatial EEG recordings that can be administered by non-specialized medical personnel in any medical center. The purpose of this study was to determine how accurate epileptologists are at remotely reviewing Epilog sensor EEG in the 10-channel "REMI montage," with and without seizure detection support software. Three board certified epileptologists reviewed the REMI montage from 20 subjects who wore four Epilog sensors for up to 5 days alongside traditional video-EEG in the EMU, 10 of whom experienced a total of 24 focal-onset electrographic seizures and 10 of whom experienced no seizures or epileptiform activity. Epileptologists randomly reviewed the same datasets with and without clinical decision support annotations from an automated seizure detection algorithm tuned to be highly sensitive. Blinded consensus review of unannotated Epilog EEG in the REMI montage detected people who were experiencing electrographic seizure activity with 90% sensitivity and 90% specificity. Consensus detection of individual focal onset seizures resulted in a mean sensitivity of 61%, precision of 80%, and false detection rate (FDR) of 0.002 false positives per hour (FP/h) of data. With algorithm seizure detection annotations, the consensus review mean sensitivity improved to 68% with a slight increase in FDR (0.005 FP/h). As seizure detection software, the automated algorithm detected people who were experiencing electrographic seizure activity with 100% sensitivity and 70% specificity, and detected individual focal onset seizures with a mean sensitivity of 90% and mean false alarm rate of 0.087 FP/h. This is the first study showing epileptologists' ability to blindly review EEG from four Epilog sensors in the REMI montage, and the results demonstrate the clinical potential to accurately identify patients experiencing electrographic seizures. Additionally, the automated algorithm shows promise as clinical decision support software to detect discrete electrographic seizures in individual records as accurately as FDA-cleared predicates.
Epitel公司开发了Epilog，这是一种微型、无线、可穿戴的脑电图（EEG）传感器。四个Epilog传感器作为Epitel远程EEG监测平台（REMI）的一部分进行组合，以创建10通道的EEG用于远程患者监测。REMI旨在提供全面的空间EEG记录，可由任何医疗中心的非专业医务人员进行管理。本研究的目的是确定癫痫专家在有和没有癫痫发作检测支持软件的情况下，对10通道“REMI蒙太奇”中Epilog传感器EEG进行远程评估的准确性。三位获得董事会认证的癫痫专家对20名佩戴四个Epilog传感器长达5天的受试者的REMI蒙太奇进行了评估，同时在癫痫监测单元中进行传统视频EEG监测，其中10人总共经历了24次局灶性发作性脑电图癫痫发作，另外10人未经历癫痫发作或癫痫样活动。癫痫专家随机评估了相同的数据集，一次有来自经过高度敏感调整的自动癫痫检测算法的临床决策支持注释，一次没有。对REMI蒙太奇中未注释的Epilog EEG进行盲法共识评估，检测出正在经历脑电图癫痫发作活动的人的灵敏度为90%，特异性为90%。对个体局灶性发作的共识检测结果为平均灵敏度61%、精确度80%，每小时数据的误报率（FDR）为0.002次误报（FP/h）。有算法癫痫发作检测注释时，共识评估的平均灵敏度提高到68%，FDR略有增加（0.005 FP/h）。作为癫痫发作检测软件，自动算法检测出正在经历脑电图癫痫发作活动的人的灵敏度为100%，特异性为70%，检测个体局灶性发作的平均灵敏度为90%，平均误报率为0.087 FP/h。这是第一项展示癫痫专家对REMI蒙太奇中四个Epilog传感器的EEG进行盲法评估能力的研究，结果证明了准确识别正在经历脑电图癫痫发作患者的临床潜力。此外，自动算法作为临床决策支持软件显示出前景，能够像FDA批准的同类产品一样准确地检测个体记录中的离散脑电图癫痫发作。
